使用改进的计算成本提高的深度学习CNN模型检测黑色素瘤皮肤癌

Gourav Ganesh, Karuppanagounder Somasundaram
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引用次数: 0

摘要

黑色素瘤一直被认为是最致命的癌症之一。黑色素瘤是一种高度恶性的皮肤癌,起源于黑色素细胞,黑色素细胞负责产生皮肤色素。它的特点是异常细胞不受控制的增殖,这些细胞有可能侵入周围组织并扩散到身体的远处。在这项工作中,我们的目标是将皮肤病分为7类。我们的目标是提出一种深度学习CNN模型,通过定制网络架构的层数和激活函数,降低计算成本,提高黑色素瘤检测的准确性。将改进后的模型与Resnet、Dense Net、Inception、VGG和Dense Net- ii等预训练模型进行了比较研究,这些模型的准确性无可挑剔。使用HAM10000数据集进行研究,我们对所提出的模型得到了较好的结果。并对其进行了图形化处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detect Melanoma Skin Cancer Using an Improved Deep Learning CNN Model with Improved Computational Costs
Melanoma cancer has been considered as one of the deadliest cancers. Melanoma is a highly malignant form of skin cancer that originates from melanocytes, the cells responsible for producing skin pigment. It is characterized by the uncontrolled prolife-elation of abnormal cells, which have the potential to invade surrounding tissues and spread to distant parts of the body. In this work, we aim to classify the skin disease into 7 classes. Our objective is to propose a deep learning CNN model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture and activation functions followed by reducing the computational cost. A comparative study is made between the improved model and the use of pre-trained Models like Resnet, Dense Net, Inception, VGG and Dense Net-II which has been giving impeccable accuracy. The HAM10000 dataset is used for research and we have got better results for the proposed model. Also, graphical results have been obtained for the same.
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